In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
data = pd.read_csv('Wine_Quality_Data.csv')
data.head()
Out[1]:
| fixed_acidity | volatile_acidity | citric_acid | residual_sugar | chlorides | free_sulfur_dioxide | total_sulfur_dioxide | density | pH | sulphates | alcohol | quality | color | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 | red |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 | red |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 | red |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |
In [2]:
data.tail()
Out[2]:
| fixed_acidity | volatile_acidity | citric_acid | residual_sugar | chlorides | free_sulfur_dioxide | total_sulfur_dioxide | density | pH | sulphates | alcohol | quality | color | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6492 | 6.2 | 0.21 | 0.29 | 1.6 | 0.039 | 24.0 | 92.0 | 0.99114 | 3.27 | 0.50 | 11.2 | 6 | white |
| 6493 | 6.6 | 0.32 | 0.36 | 8.0 | 0.047 | 57.0 | 168.0 | 0.99490 | 3.15 | 0.46 | 9.6 | 5 | white |
| 6494 | 6.5 | 0.24 | 0.19 | 1.2 | 0.041 | 30.0 | 111.0 | 0.99254 | 2.99 | 0.46 | 9.4 | 6 | white |
| 6495 | 5.5 | 0.29 | 0.30 | 1.1 | 0.022 | 20.0 | 110.0 | 0.98869 | 3.34 | 0.38 | 12.8 | 7 | white |
| 6496 | 6.0 | 0.21 | 0.38 | 0.8 | 0.020 | 22.0 | 98.0 | 0.98941 | 3.26 | 0.32 | 11.8 | 6 | white |
In [3]:
data.describe()
Out[3]:
| fixed_acidity | volatile_acidity | citric_acid | residual_sugar | chlorides | free_sulfur_dioxide | total_sulfur_dioxide | density | pH | sulphates | alcohol | quality | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 | 6497.000000 |
| mean | 7.215307 | 0.339666 | 0.318633 | 5.443235 | 0.056034 | 30.525319 | 115.744574 | 0.994697 | 3.218501 | 0.531268 | 10.491801 | 5.818378 |
| std | 1.296434 | 0.164636 | 0.145318 | 4.757804 | 0.035034 | 17.749400 | 56.521855 | 0.002999 | 0.160787 | 0.148806 | 1.192712 | 0.873255 |
| min | 3.800000 | 0.080000 | 0.000000 | 0.600000 | 0.009000 | 1.000000 | 6.000000 | 0.987110 | 2.720000 | 0.220000 | 8.000000 | 3.000000 |
| 25% | 6.400000 | 0.230000 | 0.250000 | 1.800000 | 0.038000 | 17.000000 | 77.000000 | 0.992340 | 3.110000 | 0.430000 | 9.500000 | 5.000000 |
| 50% | 7.000000 | 0.290000 | 0.310000 | 3.000000 | 0.047000 | 29.000000 | 118.000000 | 0.994890 | 3.210000 | 0.510000 | 10.300000 | 6.000000 |
| 75% | 7.700000 | 0.400000 | 0.390000 | 8.100000 | 0.065000 | 41.000000 | 156.000000 | 0.996990 | 3.320000 | 0.600000 | 11.300000 | 6.000000 |
| max | 15.900000 | 1.580000 | 1.660000 | 65.800000 | 0.611000 | 289.000000 | 440.000000 | 1.038980 | 4.010000 | 2.000000 | 14.900000 | 9.000000 |
In [4]:
y = (data['color'] == 'red').astype(int)
fields = list(data.columns[:-1]) # everything except "color"
correlations = data[fields].corrwith(y)
correlations.sort_values(inplace=True)
correlations
Out[4]:
total_sulfur_dioxide -0.700357 free_sulfur_dioxide -0.471644 residual_sugar -0.348821 citric_acid -0.187397 quality -0.119323 alcohol -0.032970 pH 0.329129 density 0.390645 fixed_acidity 0.486740 sulphates 0.487218 chlorides 0.512678 volatile_acidity 0.653036 dtype: float64
In [5]:
sns.set_context('talk')
sns.set_palette('dark')
sns.set_style('white')
sns.pairplot(data, hue='color')
Out[5]:
<seaborn.axisgrid.PairGrid at 0x26b7830ae08>
In [6]:
ax = correlations.plot(kind='bar')
ax.set(ylim=[-1, 1], ylabel='pearson correlation');
In [7]:
from sklearn.preprocessing import MinMaxScaler
fields = correlations.map(abs).sort_values().iloc[-2:].index
print(fields)
X = data[fields]
scaler = MinMaxScaler()
X = scaler.fit_transform(X)
X = pd.DataFrame(X, columns=['%s_scaled' % fld for fld in fields])
print(X.columns)
Index(['volatile_acidity', 'total_sulfur_dioxide'], dtype='object') Index(['volatile_acidity_scaled', 'total_sulfur_dioxide_scaled'], dtype='object')
Linear SVC¶
In [8]:
from sklearn.svm import LinearSVC
LSVC = LinearSVC()
LSVC.fit(X, y)
X_color = X.sample(300, random_state=45)
y_color = y.loc[X_color.index]
y_color = y_color.map(lambda r: 'red' if r == 1 else 'yellow')
ax = plt.axes()
ax.scatter(
X_color.iloc[:, 0], X_color.iloc[:, 1],
color=y_color, alpha=1)
x_axis, y_axis = np.arange(0, 1.005, .005), np.arange(0, 1.005, .005)
xx, yy = np.meshgrid(x_axis, y_axis)
xx_ravel = xx.ravel()
yy_ravel = yy.ravel()
X_grid = pd.DataFrame([xx_ravel, yy_ravel]).T
y_grid_predictions = LSVC.predict(X_grid)
y_grid_predictions = y_grid_predictions.reshape(xx.shape)
ax.contourf(xx, yy, y_grid_predictions, cmap=plt.cm.autumn_r, alpha=.3)
ax.set(xlabel=fields[0], ylabel=fields[1],
xlim=[0, 1], ylim=[0, 1],
title='decision boundary for LinearSVC');
In [10]:
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
y2_LSVC_model = LSVC.predict(X)
print("LSVC Accuracy :", accuracy_score(y, y2_LSVC_model))
LSVC Accuracy : 0.9545944281976296
Gaussian kernel SVC¶
In [11]:
def plot_decision_boundary(estimator, X, y):
estimator.fit(X, y)
X_color = X.sample(300)
y_color = y.loc[X_color.index]
y_color = y_color.map(lambda r: 'red' if r == 1 else 'yellow')
x_axis, y_axis = np.arange(0, 1, .005), np.arange(0, 1, .005)
xx, yy = np.meshgrid(x_axis, y_axis)
xx_ravel = xx.ravel()
yy_ravel = yy.ravel()
X_grid = pd.DataFrame([xx_ravel, yy_ravel]).T
y_grid_predictions = estimator.predict(X_grid)
y_grid_predictions = y_grid_predictions.reshape(xx.shape)
fig, ax = plt.subplots(figsize=(8, 5))
ax.contourf(xx, yy, y_grid_predictions, cmap=plt.cm.autumn_r, alpha=.3)
ax.scatter(X_color.iloc[:, 0], X_color.iloc[:, 1], color=y_color, alpha=1)
ax.set(xlabel=fields[0], ylabel=fields[1], title=str(estimator))
In [12]:
from sklearn.svm import SVC
gammas = [0.5, 1, 2, 10]
for gamma in gammas:
SVC_Gaussian = SVC(kernel='rbf', gamma=gamma)
plot_decision_boundary(SVC_Gaussian, X, y)
In [13]:
Cs = [.1, 1, 10]
for C in Cs:
SVC_Gaussian = SVC(kernel='rbf', gamma=2, C=C)
plot_decision_boundary(SVC_Gaussian, X, y)
In [14]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 45)
In [15]:
from sklearn.model_selection import GridSearchCV
param_grid = {'C': [0.1, 1, 10],
'gamma': [10, 2, 1, 0.5],
'kernel': ['rbf']}
grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)
grid.fit(X_train, y_train)
Fitting 5 folds for each of 12 candidates, totalling 60 fits [CV] C=0.1, gamma=10, kernel=rbf ..................................... [CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.959, total= 0.1s [CV] C=0.1, gamma=10, kernel=rbf .....................................
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.955, total= 0.1s [CV] C=0.1, gamma=10, kernel=rbf ..................................... [CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.958, total= 0.1s [CV] C=0.1, gamma=10, kernel=rbf ..................................... [CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.946, total= 0.1s [CV] C=0.1, gamma=10, kernel=rbf .....................................
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s
[CV] ......... C=0.1, gamma=10, kernel=rbf, score=0.948, total= 0.2s [CV] C=0.1, gamma=2, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=2, kernel=rbf, score=0.957, total= 0.1s [CV] C=0.1, gamma=2, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=2, kernel=rbf, score=0.956, total= 0.1s [CV] C=0.1, gamma=2, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=2, kernel=rbf, score=0.960, total= 0.1s [CV] C=0.1, gamma=2, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=2, kernel=rbf, score=0.945, total= 0.1s [CV] C=0.1, gamma=2, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=2, kernel=rbf, score=0.950, total= 0.1s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.956, total= 0.2s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.956, total= 0.2s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.958, total= 0.2s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.941, total= 0.2s [CV] C=0.1, gamma=1, kernel=rbf ...................................... [CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.952, total= 0.2s [CV] C=0.1, gamma=0.5, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.5, kernel=rbf, score=0.949, total= 0.2s [CV] C=0.1, gamma=0.5, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.5, kernel=rbf, score=0.953, total= 0.3s [CV] C=0.1, gamma=0.5, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.5, kernel=rbf, score=0.952, total= 0.3s [CV] C=0.1, gamma=0.5, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.5, kernel=rbf, score=0.937, total= 0.2s [CV] C=0.1, gamma=0.5, kernel=rbf .................................... [CV] ........ C=0.1, gamma=0.5, kernel=rbf, score=0.946, total= 0.2s [CV] C=1, gamma=10, kernel=rbf ....................................... [CV] ........... C=1, gamma=10, kernel=rbf, score=0.962, total= 0.1s [CV] C=1, gamma=10, kernel=rbf ....................................... [CV] ........... C=1, gamma=10, kernel=rbf, score=0.957, total= 0.1s [CV] C=1, gamma=10, kernel=rbf ....................................... [CV] ........... C=1, gamma=10, kernel=rbf, score=0.960, total= 0.1s [CV] C=1, gamma=10, kernel=rbf ....................................... [CV] ........... C=1, gamma=10, kernel=rbf, score=0.948, total= 0.1s [CV] C=1, gamma=10, kernel=rbf ....................................... [CV] ........... C=1, gamma=10, kernel=rbf, score=0.952, total= 0.1s [CV] C=1, gamma=2, kernel=rbf ........................................ [CV] ............ C=1, gamma=2, kernel=rbf, score=0.958, total= 0.1s [CV] C=1, gamma=2, kernel=rbf ........................................ [CV] ............ C=1, gamma=2, kernel=rbf, score=0.954, total= 0.1s [CV] C=1, gamma=2, kernel=rbf ........................................ [CV] ............ C=1, gamma=2, kernel=rbf, score=0.957, total= 0.1s [CV] C=1, gamma=2, kernel=rbf ........................................ [CV] ............ C=1, gamma=2, kernel=rbf, score=0.946, total= 0.1s [CV] C=1, gamma=2, kernel=rbf ........................................ [CV] ............ C=1, gamma=2, kernel=rbf, score=0.947, total= 0.1s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.958, total= 0.2s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.956, total= 0.1s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.957, total= 0.1s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.945, total= 0.1s [CV] C=1, gamma=1, kernel=rbf ........................................ [CV] ............ C=1, gamma=1, kernel=rbf, score=0.948, total= 0.1s [CV] C=1, gamma=0.5, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.5, kernel=rbf, score=0.957, total= 0.1s [CV] C=1, gamma=0.5, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.5, kernel=rbf, score=0.956, total= 0.1s [CV] C=1, gamma=0.5, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.5, kernel=rbf, score=0.958, total= 0.1s [CV] C=1, gamma=0.5, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.5, kernel=rbf, score=0.945, total= 0.1s [CV] C=1, gamma=0.5, kernel=rbf ...................................... [CV] .......... C=1, gamma=0.5, kernel=rbf, score=0.950, total= 0.1s [CV] C=10, gamma=10, kernel=rbf ...................................... [CV] .......... C=10, gamma=10, kernel=rbf, score=0.960, total= 0.1s [CV] C=10, gamma=10, kernel=rbf ...................................... [CV] .......... C=10, gamma=10, kernel=rbf, score=0.955, total= 0.1s [CV] C=10, gamma=10, kernel=rbf ...................................... [CV] .......... C=10, gamma=10, kernel=rbf, score=0.960, total= 0.1s [CV] C=10, gamma=10, kernel=rbf ...................................... [CV] .......... C=10, gamma=10, kernel=rbf, score=0.949, total= 0.1s [CV] C=10, gamma=10, kernel=rbf ...................................... [CV] .......... C=10, gamma=10, kernel=rbf, score=0.954, total= 0.1s [CV] C=10, gamma=2, kernel=rbf ....................................... [CV] ........... C=10, gamma=2, kernel=rbf, score=0.958, total= 0.1s [CV] C=10, gamma=2, kernel=rbf ....................................... [CV] ........... C=10, gamma=2, kernel=rbf, score=0.954, total= 0.1s [CV] C=10, gamma=2, kernel=rbf ....................................... [CV] ........... C=10, gamma=2, kernel=rbf, score=0.957, total= 0.1s [CV] C=10, gamma=2, kernel=rbf ....................................... [CV] ........... C=10, gamma=2, kernel=rbf, score=0.947, total= 0.1s [CV] C=10, gamma=2, kernel=rbf ....................................... [CV] ........... C=10, gamma=2, kernel=rbf, score=0.946, total= 0.1s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.958, total= 0.1s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.955, total= 0.1s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.957, total= 0.1s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.946, total= 0.1s [CV] C=10, gamma=1, kernel=rbf ....................................... [CV] ........... C=10, gamma=1, kernel=rbf, score=0.947, total= 0.1s [CV] C=10, gamma=0.5, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.5, kernel=rbf, score=0.958, total= 0.1s [CV] C=10, gamma=0.5, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.5, kernel=rbf, score=0.955, total= 0.1s [CV] C=10, gamma=0.5, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.5, kernel=rbf, score=0.957, total= 0.1s [CV] C=10, gamma=0.5, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.5, kernel=rbf, score=0.946, total= 0.1s [CV] C=10, gamma=0.5, kernel=rbf ..................................... [CV] ......... C=10, gamma=0.5, kernel=rbf, score=0.948, total= 0.1s
[Parallel(n_jobs=1)]: Done 60 out of 60 | elapsed: 6.8s finished
Out[15]:
GridSearchCV(cv=None, error_score=nan,
estimator=SVC(C=1.0, break_ties=False, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='scale', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='deprecated', n_jobs=None,
param_grid={'C': [0.1, 1, 10], 'gamma': [10, 2, 1, 0.5],
'kernel': ['rbf']},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=3)
In [16]:
print(grid.best_params_)
print(grid.best_estimator_)
{'C': 10, 'gamma': 10, 'kernel': 'rbf'}
SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=10, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
In [17]:
grid_predictions = grid.predict(X_test)
print(classification_report(y_test, grid_predictions))
precision recall f1-score support
0 0.97 0.97 0.97 1447
1 0.92 0.90 0.91 503
accuracy 0.95 1950
macro avg 0.94 0.94 0.94 1950
weighted avg 0.95 0.95 0.95 1950
Polynomial Kernel¶
In [18]:
from sklearn.svm import SVC
gammas = [0.5, 1, 2, 10]
for gamma in gammas:
SVC_Polynomial = SVC(kernel='poly', gamma=gamma)
plot_decision_boundary(SVC_Polynomial, X, y)
In [19]:
Cs = [10,20,100,200]
for C in Cs:
SVC_Polynomial = SVC(kernel='poly', gamma=2, C=C)
plot_decision_boundary(SVC_Polynomial, X, y)
In [20]:
param_grid = {'C': [10, 20, 100, 200],
'gamma': [10, 2, 1, 0.5],
'kernel': ['poly']}
grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)
grid.fit(X_train, y_train)
Fitting 5 folds for each of 16 candidates, totalling 80 fits [CV] C=10, gamma=10, kernel=poly ..................................... [CV] ......... C=10, gamma=10, kernel=poly, score=0.929, total= 0.1s [CV] C=10, gamma=10, kernel=poly .....................................
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[CV] ......... C=10, gamma=10, kernel=poly, score=0.936, total= 0.1s [CV] C=10, gamma=10, kernel=poly ..................................... [CV] ......... C=10, gamma=10, kernel=poly, score=0.936, total= 0.1s [CV] C=10, gamma=10, kernel=poly .....................................
[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s
[CV] ......... C=10, gamma=10, kernel=poly, score=0.945, total= 0.1s [CV] C=10, gamma=10, kernel=poly ..................................... [CV] ......... C=10, gamma=10, kernel=poly, score=0.932, total= 0.1s [CV] C=10, gamma=2, kernel=poly ...................................... [CV] .......... C=10, gamma=2, kernel=poly, score=0.921, total= 0.1s [CV] C=10, gamma=2, kernel=poly ...................................... [CV] .......... C=10, gamma=2, kernel=poly, score=0.932, total= 0.1s [CV] C=10, gamma=2, kernel=poly ...................................... [CV] .......... C=10, gamma=2, kernel=poly, score=0.931, total= 0.1s [CV] C=10, gamma=2, kernel=poly ...................................... [CV] .......... C=10, gamma=2, kernel=poly, score=0.922, total= 0.1s [CV] C=10, gamma=2, kernel=poly ...................................... [CV] .......... C=10, gamma=2, kernel=poly, score=0.922, total= 0.1s [CV] C=10, gamma=1, kernel=poly ...................................... [CV] .......... C=10, gamma=1, kernel=poly, score=0.886, total= 0.1s [CV] C=10, gamma=1, kernel=poly ...................................... [CV] .......... C=10, gamma=1, kernel=poly, score=0.890, total= 0.1s [CV] C=10, gamma=1, kernel=poly ...................................... [CV] .......... C=10, gamma=1, kernel=poly, score=0.899, total= 0.1s [CV] C=10, gamma=1, kernel=poly ...................................... [CV] .......... C=10, gamma=1, kernel=poly, score=0.889, total= 0.1s [CV] C=10, gamma=1, kernel=poly ...................................... [CV] .......... C=10, gamma=1, kernel=poly, score=0.886, total= 0.1s [CV] C=10, gamma=0.5, kernel=poly .................................... [CV] ........ C=10, gamma=0.5, kernel=poly, score=0.819, total= 0.1s [CV] C=10, gamma=0.5, kernel=poly .................................... [CV] ........ C=10, gamma=0.5, kernel=poly, score=0.829, total= 0.1s [CV] C=10, gamma=0.5, kernel=poly .................................... [CV] ........ C=10, gamma=0.5, kernel=poly, score=0.827, total= 0.1s [CV] C=10, gamma=0.5, kernel=poly .................................... [CV] ........ C=10, gamma=0.5, kernel=poly, score=0.812, total= 0.2s [CV] C=10, gamma=0.5, kernel=poly .................................... [CV] ........ C=10, gamma=0.5, kernel=poly, score=0.822, total= 0.1s [CV] C=20, gamma=10, kernel=poly ..................................... [CV] ......... C=20, gamma=10, kernel=poly, score=0.927, total= 0.2s [CV] C=20, gamma=10, kernel=poly ..................................... [CV] ......... C=20, gamma=10, kernel=poly, score=0.934, total= 0.2s [CV] C=20, gamma=10, kernel=poly ..................................... [CV] ......... C=20, gamma=10, kernel=poly, score=0.934, total= 0.2s [CV] C=20, gamma=10, kernel=poly ..................................... [CV] ......... C=20, gamma=10, kernel=poly, score=0.943, total= 0.2s [CV] C=20, gamma=10, kernel=poly ..................................... [CV] ......... C=20, gamma=10, kernel=poly, score=0.932, total= 0.1s [CV] C=20, gamma=2, kernel=poly ...................................... [CV] .......... C=20, gamma=2, kernel=poly, score=0.929, total= 0.1s [CV] C=20, gamma=2, kernel=poly ...................................... [CV] .......... C=20, gamma=2, kernel=poly, score=0.940, total= 0.1s [CV] C=20, gamma=2, kernel=poly ...................................... [CV] .......... C=20, gamma=2, kernel=poly, score=0.938, total= 0.1s [CV] C=20, gamma=2, kernel=poly ...................................... [CV] .......... C=20, gamma=2, kernel=poly, score=0.932, total= 0.1s [CV] C=20, gamma=2, kernel=poly ...................................... [CV] .......... C=20, gamma=2, kernel=poly, score=0.925, total= 0.1s [CV] C=20, gamma=1, kernel=poly ...................................... [CV] .......... C=20, gamma=1, kernel=poly, score=0.902, total= 0.1s [CV] C=20, gamma=1, kernel=poly ...................................... [CV] .......... C=20, gamma=1, kernel=poly, score=0.905, total= 0.1s [CV] C=20, gamma=1, kernel=poly ...................................... [CV] .......... C=20, gamma=1, kernel=poly, score=0.905, total= 0.1s [CV] C=20, gamma=1, kernel=poly ...................................... [CV] .......... C=20, gamma=1, kernel=poly, score=0.900, total= 0.1s [CV] C=20, gamma=1, kernel=poly ...................................... [CV] .......... C=20, gamma=1, kernel=poly, score=0.895, total= 0.1s [CV] C=20, gamma=0.5, kernel=poly .................................... [CV] ........ C=20, gamma=0.5, kernel=poly, score=0.845, total= 0.1s [CV] C=20, gamma=0.5, kernel=poly .................................... [CV] ........ C=20, gamma=0.5, kernel=poly, score=0.859, total= 0.1s [CV] C=20, gamma=0.5, kernel=poly .................................... [CV] ........ C=20, gamma=0.5, kernel=poly, score=0.865, total= 0.2s [CV] C=20, gamma=0.5, kernel=poly .................................... [CV] ........ C=20, gamma=0.5, kernel=poly, score=0.854, total= 0.1s [CV] C=20, gamma=0.5, kernel=poly .................................... [CV] ........ C=20, gamma=0.5, kernel=poly, score=0.855, total= 0.2s [CV] C=100, gamma=10, kernel=poly .................................... [CV] ........ C=100, gamma=10, kernel=poly, score=0.927, total= 0.5s [CV] C=100, gamma=10, kernel=poly .................................... [CV] ........ C=100, gamma=10, kernel=poly, score=0.932, total= 0.3s [CV] C=100, gamma=10, kernel=poly .................................... [CV] ........ C=100, gamma=10, kernel=poly, score=0.935, total= 0.5s [CV] C=100, gamma=10, kernel=poly .................................... [CV] ........ C=100, gamma=10, kernel=poly, score=0.942, total= 0.3s [CV] C=100, gamma=10, kernel=poly .................................... [CV] ........ C=100, gamma=10, kernel=poly, score=0.931, total= 0.4s [CV] C=100, gamma=2, kernel=poly ..................................... [CV] ......... C=100, gamma=2, kernel=poly, score=0.933, total= 0.1s [CV] C=100, gamma=2, kernel=poly ..................................... [CV] ......... C=100, gamma=2, kernel=poly, score=0.946, total= 0.1s [CV] C=100, gamma=2, kernel=poly ..................................... [CV] ......... C=100, gamma=2, kernel=poly, score=0.942, total= 0.1s [CV] C=100, gamma=2, kernel=poly ..................................... [CV] ......... C=100, gamma=2, kernel=poly, score=0.944, total= 0.1s [CV] C=100, gamma=2, kernel=poly ..................................... [CV] ......... C=100, gamma=2, kernel=poly, score=0.931, total= 0.1s [CV] C=100, gamma=1, kernel=poly ..................................... [CV] ......... C=100, gamma=1, kernel=poly, score=0.923, total= 0.1s [CV] C=100, gamma=1, kernel=poly ..................................... [CV] ......... C=100, gamma=1, kernel=poly, score=0.933, total= 0.1s [CV] C=100, gamma=1, kernel=poly ..................................... [CV] ......... C=100, gamma=1, kernel=poly, score=0.932, total= 0.1s [CV] C=100, gamma=1, kernel=poly ..................................... [CV] ......... C=100, gamma=1, kernel=poly, score=0.924, total= 0.1s [CV] C=100, gamma=1, kernel=poly ..................................... [CV] ......... C=100, gamma=1, kernel=poly, score=0.924, total= 0.1s [CV] C=100, gamma=0.5, kernel=poly ................................... [CV] ....... C=100, gamma=0.5, kernel=poly, score=0.891, total= 0.1s [CV] C=100, gamma=0.5, kernel=poly ................................... [CV] ....... C=100, gamma=0.5, kernel=poly, score=0.897, total= 0.1s [CV] C=100, gamma=0.5, kernel=poly ................................... [CV] ....... C=100, gamma=0.5, kernel=poly, score=0.900, total= 0.1s [CV] C=100, gamma=0.5, kernel=poly ................................... [CV] ....... C=100, gamma=0.5, kernel=poly, score=0.891, total= 0.1s [CV] C=100, gamma=0.5, kernel=poly ................................... [CV] ....... C=100, gamma=0.5, kernel=poly, score=0.887, total= 0.1s [CV] C=200, gamma=10, kernel=poly .................................... [CV] ........ C=200, gamma=10, kernel=poly, score=0.927, total= 0.5s [CV] C=200, gamma=10, kernel=poly .................................... [CV] ........ C=200, gamma=10, kernel=poly, score=0.932, total= 0.5s [CV] C=200, gamma=10, kernel=poly .................................... [CV] ........ C=200, gamma=10, kernel=poly, score=0.935, total= 0.9s [CV] C=200, gamma=10, kernel=poly .................................... [CV] ........ C=200, gamma=10, kernel=poly, score=0.942, total= 0.6s [CV] C=200, gamma=10, kernel=poly .................................... [CV] ........ C=200, gamma=10, kernel=poly, score=0.928, total= 0.6s [CV] C=200, gamma=2, kernel=poly ..................................... [CV] ......... C=200, gamma=2, kernel=poly, score=0.933, total= 0.1s [CV] C=200, gamma=2, kernel=poly ..................................... [CV] ......... C=200, gamma=2, kernel=poly, score=0.947, total= 0.1s [CV] C=200, gamma=2, kernel=poly ..................................... [CV] ......... C=200, gamma=2, kernel=poly, score=0.941, total= 0.1s [CV] C=200, gamma=2, kernel=poly ..................................... [CV] ......... C=200, gamma=2, kernel=poly, score=0.945, total= 0.1s [CV] C=200, gamma=2, kernel=poly ..................................... [CV] ......... C=200, gamma=2, kernel=poly, score=0.932, total= 0.1s [CV] C=200, gamma=1, kernel=poly ..................................... [CV] ......... C=200, gamma=1, kernel=poly, score=0.931, total= 0.1s [CV] C=200, gamma=1, kernel=poly ..................................... [CV] ......... C=200, gamma=1, kernel=poly, score=0.941, total= 0.1s [CV] C=200, gamma=1, kernel=poly ..................................... [CV] ......... C=200, gamma=1, kernel=poly, score=0.939, total= 0.1s [CV] C=200, gamma=1, kernel=poly ..................................... [CV] ......... C=200, gamma=1, kernel=poly, score=0.933, total= 0.1s [CV] C=200, gamma=1, kernel=poly ..................................... [CV] ......... C=200, gamma=1, kernel=poly, score=0.928, total= 0.1s [CV] C=200, gamma=0.5, kernel=poly ................................... [CV] ....... C=200, gamma=0.5, kernel=poly, score=0.902, total= 0.1s [CV] C=200, gamma=0.5, kernel=poly ................................... [CV] ....... C=200, gamma=0.5, kernel=poly, score=0.904, total= 0.1s [CV] C=200, gamma=0.5, kernel=poly ................................... [CV] ....... C=200, gamma=0.5, kernel=poly, score=0.913, total= 0.1s [CV] C=200, gamma=0.5, kernel=poly ................................... [CV] ....... C=200, gamma=0.5, kernel=poly, score=0.904, total= 0.1s [CV] C=200, gamma=0.5, kernel=poly ................................... [CV] ....... C=200, gamma=0.5, kernel=poly, score=0.902, total= 0.1s
[Parallel(n_jobs=1)]: Done 80 out of 80 | elapsed: 12.5s finished
Out[20]:
GridSearchCV(cv=None, error_score=nan,
estimator=SVC(C=1.0, break_ties=False, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='scale', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='deprecated', n_jobs=None,
param_grid={'C': [10, 20, 100, 200], 'gamma': [10, 2, 1, 0.5],
'kernel': ['poly']},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=3)
In [21]:
print(grid.best_params_)
print(grid.best_estimator_)
{'C': 200, 'gamma': 2, 'kernel': 'poly'}
SVC(C=200, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=2, kernel='poly',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
In [22]:
grid_predictions = grid.predict(X_test)
print(classification_report(y_test, grid_predictions))
precision recall f1-score support
0 0.93 0.98 0.95 1447
1 0.94 0.77 0.85 503
accuracy 0.93 1950
macro avg 0.93 0.88 0.90 1950
weighted avg 0.93 0.93 0.93 1950
Sigmoid Kernel¶
In [23]:
gammas = [0.5, 1, 2, 10]
for gamma in gammas:
SVC_Sigmoid = SVC(kernel='sigmoid', gamma=gamma)
plot_decision_boundary(SVC_Sigmoid, X, y)
In [24]:
Cs = [10,20,100,200]
for C in Cs:
SVC_Sigmoid = SVC(kernel='sigmoid', gamma=2, C=C)
plot_decision_boundary(SVC_Sigmoid, X, y)
In [25]:
param_grid = {'C': [10, 20, 100, 200],
'gamma': [10, 2, 1, 0.5],
'kernel': ['sigmoid']}
grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)
grid.fit(X_train, y_train)
Fitting 5 folds for each of 16 candidates, totalling 80 fits [CV] C=10, gamma=10, kernel=sigmoid .................................. [CV] ...... C=10, gamma=10, kernel=sigmoid, score=0.897, total= 0.1s [CV] C=10, gamma=10, kernel=sigmoid .................................. [CV] ...... C=10, gamma=10, kernel=sigmoid, score=0.887, total= 0.1s [CV] C=10, gamma=10, kernel=sigmoid ..................................
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s
[CV] ...... C=10, gamma=10, kernel=sigmoid, score=0.870, total= 0.1s [CV] C=10, gamma=10, kernel=sigmoid .................................. [CV] ...... C=10, gamma=10, kernel=sigmoid, score=0.892, total= 0.1s [CV] C=10, gamma=10, kernel=sigmoid .................................. [CV] ...... C=10, gamma=10, kernel=sigmoid, score=0.910, total= 0.1s [CV] C=10, gamma=2, kernel=sigmoid ................................... [CV] ....... C=10, gamma=2, kernel=sigmoid, score=0.948, total= 0.1s [CV] C=10, gamma=2, kernel=sigmoid ................................... [CV] ....... C=10, gamma=2, kernel=sigmoid, score=0.946, total= 0.1s [CV] C=10, gamma=2, kernel=sigmoid ................................... [CV] ....... C=10, gamma=2, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=10, gamma=2, kernel=sigmoid ................................... [CV] ....... C=10, gamma=2, kernel=sigmoid, score=0.938, total= 0.1s [CV] C=10, gamma=2, kernel=sigmoid ................................... [CV] ....... C=10, gamma=2, kernel=sigmoid, score=0.936, total= 0.1s [CV] C=10, gamma=1, kernel=sigmoid ................................... [CV] ....... C=10, gamma=1, kernel=sigmoid, score=0.960, total= 0.1s [CV] C=10, gamma=1, kernel=sigmoid ................................... [CV] ....... C=10, gamma=1, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=10, gamma=1, kernel=sigmoid ................................... [CV] ....... C=10, gamma=1, kernel=sigmoid, score=0.958, total= 0.1s [CV] C=10, gamma=1, kernel=sigmoid ................................... [CV] ....... C=10, gamma=1, kernel=sigmoid, score=0.945, total= 0.1s [CV] C=10, gamma=1, kernel=sigmoid ................................... [CV] ....... C=10, gamma=1, kernel=sigmoid, score=0.949, total= 0.1s [CV] C=10, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=10, gamma=0.5, kernel=sigmoid, score=0.960, total= 0.1s [CV] C=10, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=10, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=10, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=10, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=10, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=10, gamma=0.5, kernel=sigmoid, score=0.946, total= 0.1s [CV] C=10, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=10, gamma=0.5, kernel=sigmoid, score=0.948, total= 0.1s [CV] C=20, gamma=10, kernel=sigmoid .................................. [CV] ...... C=20, gamma=10, kernel=sigmoid, score=0.897, total= 0.1s [CV] C=20, gamma=10, kernel=sigmoid .................................. [CV] ...... C=20, gamma=10, kernel=sigmoid, score=0.887, total= 0.1s [CV] C=20, gamma=10, kernel=sigmoid .................................. [CV] ...... C=20, gamma=10, kernel=sigmoid, score=0.870, total= 0.1s [CV] C=20, gamma=10, kernel=sigmoid .................................. [CV] ...... C=20, gamma=10, kernel=sigmoid, score=0.891, total= 0.1s [CV] C=20, gamma=10, kernel=sigmoid .................................. [CV] ...... C=20, gamma=10, kernel=sigmoid, score=0.910, total= 0.1s [CV] C=20, gamma=2, kernel=sigmoid ................................... [CV] ....... C=20, gamma=2, kernel=sigmoid, score=0.943, total= 0.1s [CV] C=20, gamma=2, kernel=sigmoid ................................... [CV] ....... C=20, gamma=2, kernel=sigmoid, score=0.942, total= 0.1s [CV] C=20, gamma=2, kernel=sigmoid ................................... [CV] ....... C=20, gamma=2, kernel=sigmoid, score=0.952, total= 0.0s [CV] C=20, gamma=2, kernel=sigmoid ................................... [CV] ....... C=20, gamma=2, kernel=sigmoid, score=0.930, total= 0.1s [CV] C=20, gamma=2, kernel=sigmoid ................................... [CV] ....... C=20, gamma=2, kernel=sigmoid, score=0.936, total= 0.1s [CV] C=20, gamma=1, kernel=sigmoid ................................... [CV] ....... C=20, gamma=1, kernel=sigmoid, score=0.959, total= 0.1s [CV] C=20, gamma=1, kernel=sigmoid ................................... [CV] ....... C=20, gamma=1, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=20, gamma=1, kernel=sigmoid ................................... [CV] ....... C=20, gamma=1, kernel=sigmoid, score=0.957, total= 0.2s [CV] C=20, gamma=1, kernel=sigmoid ................................... [CV] ....... C=20, gamma=1, kernel=sigmoid, score=0.946, total= 0.1s [CV] C=20, gamma=1, kernel=sigmoid ................................... [CV] ....... C=20, gamma=1, kernel=sigmoid, score=0.950, total= 0.1s [CV] C=20, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=20, gamma=0.5, kernel=sigmoid, score=0.960, total= 0.1s [CV] C=20, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=20, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=20, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=20, gamma=0.5, kernel=sigmoid, score=0.958, total= 0.1s [CV] C=20, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=20, gamma=0.5, kernel=sigmoid, score=0.945, total= 0.1s [CV] C=20, gamma=0.5, kernel=sigmoid ................................. [CV] ..... C=20, gamma=0.5, kernel=sigmoid, score=0.949, total= 0.1s [CV] C=100, gamma=10, kernel=sigmoid ................................. [CV] ..... C=100, gamma=10, kernel=sigmoid, score=0.897, total= 0.1s [CV] C=100, gamma=10, kernel=sigmoid ................................. [CV] ..... C=100, gamma=10, kernel=sigmoid, score=0.887, total= 0.1s [CV] C=100, gamma=10, kernel=sigmoid ................................. [CV] ..... C=100, gamma=10, kernel=sigmoid, score=0.870, total= 0.1s [CV] C=100, gamma=10, kernel=sigmoid ................................. [CV] ..... C=100, gamma=10, kernel=sigmoid, score=0.891, total= 0.1s [CV] C=100, gamma=10, kernel=sigmoid ................................. [CV] ..... C=100, gamma=10, kernel=sigmoid, score=0.910, total= 0.1s [CV] C=100, gamma=2, kernel=sigmoid .................................. [CV] ...... C=100, gamma=2, kernel=sigmoid, score=0.943, total= 0.0s [CV] C=100, gamma=2, kernel=sigmoid .................................. [CV] ...... C=100, gamma=2, kernel=sigmoid, score=0.944, total= 0.1s [CV] C=100, gamma=2, kernel=sigmoid .................................. [CV] ...... C=100, gamma=2, kernel=sigmoid, score=0.946, total= 0.0s [CV] C=100, gamma=2, kernel=sigmoid .................................. [CV] ...... C=100, gamma=2, kernel=sigmoid, score=0.930, total= 0.0s [CV] C=100, gamma=2, kernel=sigmoid .................................. [CV] ...... C=100, gamma=2, kernel=sigmoid, score=0.937, total= 0.1s [CV] C=100, gamma=1, kernel=sigmoid .................................. [CV] ...... C=100, gamma=1, kernel=sigmoid, score=0.947, total= 0.1s [CV] C=100, gamma=1, kernel=sigmoid .................................. [CV] ...... C=100, gamma=1, kernel=sigmoid, score=0.946, total= 0.0s [CV] C=100, gamma=1, kernel=sigmoid .................................. [CV] ...... C=100, gamma=1, kernel=sigmoid, score=0.958, total= 0.1s [CV] C=100, gamma=1, kernel=sigmoid .................................. [CV] ...... C=100, gamma=1, kernel=sigmoid, score=0.935, total= 0.1s [CV] C=100, gamma=1, kernel=sigmoid .................................. [CV] ...... C=100, gamma=1, kernel=sigmoid, score=0.937, total= 0.1s [CV] C=100, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=100, gamma=0.5, kernel=sigmoid, score=0.960, total= 0.1s [CV] C=100, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=100, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=100, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=100, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=100, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=100, gamma=0.5, kernel=sigmoid, score=0.946, total= 0.1s [CV] C=100, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=100, gamma=0.5, kernel=sigmoid, score=0.949, total= 0.1s [CV] C=200, gamma=10, kernel=sigmoid ................................. [CV] ..... C=200, gamma=10, kernel=sigmoid, score=0.897, total= 0.1s [CV] C=200, gamma=10, kernel=sigmoid ................................. [CV] ..... C=200, gamma=10, kernel=sigmoid, score=0.887, total= 0.1s [CV] C=200, gamma=10, kernel=sigmoid ................................. [CV] ..... C=200, gamma=10, kernel=sigmoid, score=0.870, total= 0.1s [CV] C=200, gamma=10, kernel=sigmoid ................................. [CV] ..... C=200, gamma=10, kernel=sigmoid, score=0.891, total= 0.1s [CV] C=200, gamma=10, kernel=sigmoid ................................. [CV] ..... C=200, gamma=10, kernel=sigmoid, score=0.910, total= 0.1s [CV] C=200, gamma=2, kernel=sigmoid .................................. [CV] ...... C=200, gamma=2, kernel=sigmoid, score=0.943, total= 0.0s [CV] C=200, gamma=2, kernel=sigmoid .................................. [CV] ...... C=200, gamma=2, kernel=sigmoid, score=0.944, total= 0.1s [CV] C=200, gamma=2, kernel=sigmoid .................................. [CV] ...... C=200, gamma=2, kernel=sigmoid, score=0.946, total= 0.0s [CV] C=200, gamma=2, kernel=sigmoid .................................. [CV] ...... C=200, gamma=2, kernel=sigmoid, score=0.931, total= 0.0s [CV] C=200, gamma=2, kernel=sigmoid .................................. [CV] ...... C=200, gamma=2, kernel=sigmoid, score=0.937, total= 0.0s [CV] C=200, gamma=1, kernel=sigmoid .................................. [CV] ...... C=200, gamma=1, kernel=sigmoid, score=0.943, total= 0.0s [CV] C=200, gamma=1, kernel=sigmoid .................................. [CV] ...... C=200, gamma=1, kernel=sigmoid, score=0.944, total= 0.1s [CV] C=200, gamma=1, kernel=sigmoid .................................. [CV] ...... C=200, gamma=1, kernel=sigmoid, score=0.954, total= 0.0s [CV] C=200, gamma=1, kernel=sigmoid .................................. [CV] ...... C=200, gamma=1, kernel=sigmoid, score=0.935, total= 0.1s [CV] C=200, gamma=1, kernel=sigmoid .................................. [CV] ...... C=200, gamma=1, kernel=sigmoid, score=0.938, total= 0.1s [CV] C=200, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=200, gamma=0.5, kernel=sigmoid, score=0.959, total= 0.1s [CV] C=200, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=200, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=200, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=200, gamma=0.5, kernel=sigmoid, score=0.957, total= 0.1s [CV] C=200, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=200, gamma=0.5, kernel=sigmoid, score=0.946, total= 0.1s [CV] C=200, gamma=0.5, kernel=sigmoid ................................ [CV] .... C=200, gamma=0.5, kernel=sigmoid, score=0.950, total= 0.1s
[Parallel(n_jobs=1)]: Done 80 out of 80 | elapsed: 6.5s finished
Out[25]:
GridSearchCV(cv=None, error_score=nan,
estimator=SVC(C=1.0, break_ties=False, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='scale', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='deprecated', n_jobs=None,
param_grid={'C': [10, 20, 100, 200], 'gamma': [10, 2, 1, 0.5],
'kernel': ['sigmoid']},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=3)
In [26]:
print(grid.best_params_)
print(grid.best_estimator_)
{'C': 20, 'gamma': 1, 'kernel': 'sigmoid'}
SVC(C=20, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=1, kernel='sigmoid',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
In [27]:
grid_predictions = grid.predict(X_test)
print(classification_report(y_test, grid_predictions))
precision recall f1-score support
0 0.96 0.98 0.97 1447
1 0.93 0.90 0.91 503
accuracy 0.95 1950
macro avg 0.95 0.94 0.94 1950
weighted avg 0.95 0.95 0.95 1950
In [ ]: